All notable changes to the Enterprise AI Assistant Platform will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Performance optimizations for vector search
- Enhanced error handling and logging
- Additional test coverage
- Improved API response formats
- Updated dependencies to latest versions
- Memory leaks in long-running processes
- Database connection timeout issues
- Multi-agent AI system with specialized agents
- Research Agent for information gathering
- Data Analysis Agent for data processing
- Content Generation Agent for content creation
- Crew Manager for workflow orchestration
- RAG (Retrieval Augmented Generation) system
- ChromaDB vector database integration
- Pinecone support
- Document ingestion and processing
- Semantic search capabilities
- Knowledge Graph system
- Neo4j integration
- Entity and relationship management
- Graph analytics and querying
- LLM Provider Management
- OpenAI API integration
- Anthropic Claude integration
- Google AI integration
- Provider abstraction layer
- Load balancing and fallback mechanisms
- MLOps and Monitoring
- MLflow integration for experiment tracking
- Prometheus metrics collection
- Health check endpoints
- Structured logging with correlation IDs
- Comprehensive API endpoints
- Multi-agent workflow endpoints
- RAG query and ingestion endpoints
- Knowledge graph management endpoints
- Health and monitoring endpoints
- Complete test suite
- Unit tests with 90%+ coverage
- Integration tests for component interactions
- End-to-end tests for complete workflows
- Performance and load testing
- Docker deployment support
- Multi-service Docker Compose setup
- Production-ready configurations
- Health checks and monitoring
- Comprehensive documentation
- API reference and examples
- Deployment guides for various environments
- Architecture decision records
- FAQ and troubleshooting guides
- Python 3.11+ support
- FastAPI web framework
- Pydantic for data validation
- Async/await throughout the application
- Structured logging with JSON output
- Type hints for all functions and classes
- Comprehensive error handling
- Rate limiting and security measures
- Neo4j 5.15 for knowledge graph storage
- ChromaDB for vector embeddings
- PostgreSQL for relational data
- MLflow for model and experiment tracking
- Prometheus for metrics collection
- Docker for containerization
- Input validation and sanitization
- Rate limiting on API endpoints
- Secure secret management
- CORS configuration
- Error message sanitization
- Connection pooling for databases
- Async operations throughout
- Efficient vector search algorithms
- Caching strategies for frequently accessed data
- Background task processing
- Initial multi-agent architecture implementation
- Basic RAG system with ChromaDB
- Neo4j knowledge graph integration
- FastAPI application structure
- Docker Compose development environment
- Migrated from synchronous to asynchronous operations
- Improved error handling and logging
- Database connection issues
- Memory management in vector operations
- Research Agent implementation
- Data Analysis Agent basic functionality
- LLM provider abstraction layer
- Initial API endpoints
- Basic test structure
- Refactored agent architecture
- Improved configuration management
- Project scaffolding and structure
- Core configuration system
- Logging infrastructure
- Development environment setup
- Initial architecture design
- Technology stack selection
- Project conception and planning
- Technology research and evaluation
- Architecture design documents
- Initial requirements gathering